Storage States in Ultracold Collective Atoms

We present a complete theoretical description of atomic storage states in the multimode framework by including spatial coherence in atomic collective operators and atomic storage states. We show that atomic storage states are Dicke states with the maximum cooperation number. In some limits, a set of multimode atomic storage states has been established in correspondence with multimode Fock states of the electromagnetic field. This gives better understanding of both the quantum and coherent information of optical field can be preserved and recovered in ultracold medium. In this treatment, we discuss in detail both the adiabatic and dynamic transfer of quantum information between the field and the ultracold medium.Comment: 22 pages, no figures;to be published in Euro. Phys. J.

Computational Study of the Magnetic Structure of Na2_2IrO3_3

The magnetic structure of honeycomb iridate Na$_2$IrO$_3$ is of paramount importance to its exotic properties. The magnetic order is established experimentally to be zigzag antiferromagnetic. However, the previous assignment of ordered moment to the $\bm{a}$-axis is tentative. We examine the magnetic structure of Na$_{2}$IrO$_{3}$ using first-principles methods. Our calculations reveal that total energy is minimized when the zigzag antiferromagnetic order is magnetized along $\bm{g}\approx\bm{a}+\bm{c}$. Such a magnetic configuration is explained by adding anisotropic interactions to the nearest-neighbor Kitaev-Heisenberg model. Spin-wave spectrum is also calculated, where the calculated spin gap of $10.4$ meV can in principle be measured by future inelastic neutron scattering experiments. Finally we emphasize that our proposal is consistent with all known experimental evidence, including the most relevant resonant x-ray magnetic scattering measurements [X. Liu \emph{et al.} {Phys. Rev. B} \textbf{83}, 220403(R) (2011)].Comment: 18 pages, 7 figure

Weekly: A Design System that Combines Graphic and Interaction Design for Scheduling Student Events

A common need for students is scheduling events efficiently. Students using various methods for scheduling events. Some prefer a physical paper calendar, while others prefer to use a mobile device. Several problems can occur when students schedule events. For example, it may not be clear when a student’s free time periods are, especially when trying to meet with a team or group members. Also, user’s preferences are different on how they schedule events. To address the issue of inefficiency in scheduling events, a design system that focuses on print, interaction, and user experience design is created. The design system includes two parts: a printed planner, and an interactive App for smart devices. The users choose the method they want to input events and share their schedule. The Weekly App can also automatically generate the common free time of the team. The goal of this project is to combine traditional graphic and interaction design for scheduling events simply and efficiently for students. This project takes the form of a prototype. Visual and interaction design principles are integrated and design software is used to produce an efficient design system. The outcome is modified and improved based on research and usability testing to demonstrate the concepts of this project

Toward Data Efficient Online Sequential Learning

Can machines optimally take sequential decisions over time? Since decades, researchers have been seeking an answer to this question, with the ultimate goal of unlocking the potential of artificial general intelligence (AGI) for a better and sustainable society. Many are the sectors that would be boosted by machines being able to take efficient sequential decisions over time. Let think at real-world applications such as personalized systems in entertainment (content systems) but also in healthcare (personalized therapy), smart cities (traffic control, flooding prevention), robots (control and planning), etc.. However, letting machines taking proper decisions in real-life is a highly challenging task. This is caused by the uncertainty behind such decisions (uncertainty on the actual reward, on the context, on the environment, etc.). A viable solution is to learn by experience (i.e., by trial and error), letting the machines uncover the uncertainty while taking decisions, and refining its strategy accordingly. However, such refinement is usually highly data-hungry (data-inefficiency), requiring a large amount of application specified data, leading to very slow learning processes -- hence very slow convergence to optimal strategies (curse of dimensionality). Luckily, data is usually intrinsically structured. Identifying and exploiting such structure substantially improves the data-efficiency of sequential learning algorithms. This is the key hypothesis underpinning the research in this thesis, in which novel structural learning methodologies are proposed for decision-making strategies problems such as Recommendation System (RS), Multi-armed Bandit (MAB) and Reinforcement Learning (RL), with the ultimate goal of making the learning process more (data)-efficient. Specifically, we tackle such goal from the perspective of modelling the problem structure as graphs, embedding tools from graph signal processing into decision learning theory. As the first step, we study the application of graph-clustering techniques for RS, in which the curse of dimensionality is addressed by grouping data into clusters via graph-clustering techniques. Next, we exploit spectral graph structure for MAB problems, representing online learning problems. A key challenge is to learn sequentially the unknown bandit vector. Exploiting the smoothness-prior (i.e., bandit vector smooth on a given underpinning graph), we study theoretically the Laplacian-regularized estimator and provide both empirical evidences and theoretical analysis on the benefits of exploiting the graph structure in MABs. Then, we focus on the theoretical understanding of the Laplacian-regularized estimator. To this end, we derive a theoretical error upper bound on the estimator, which illustrates the impact of the alignment between the data and the graph structure as well as the graph spectrum on the estimation accuracy. We then move to RL problems, focusing on the specific problem of learning a proper representation of the state-action (representation learning problem). Motivated by the fact that a good representation should be informative of the value function, we seek a learning algorithm able to preserve continuity between the value function and the representation space. Showing that state values are intrinsically correlated to the state transition dynamic structure and the diffusion of the reward on the MDP graph, we build a new loss function based on the newly defined diffusion distance and we propose a novel method to learn state representation with such desirable property. In summary, in this thesis we address both theoretically and empirically important online sequential learning problems leveraging on the intrinsic data structure, showing the gain of the proposed solutions toward more data-efficient sequential learning strategies

Pyrolysis and catalytic pyrolysis of protein- and lipid-rich feedstock

Current biorefineries utilize only sugars or lipids in biomass for fuel production, leaving protein-rich residues underutilized. Improper disposal of those residues may cause economical or ecological problem. Research in this dissertation focuses on developing pyrolysis/catalytic pyrolysis as a pathway for producing biofuel or bio-based chemical from protein- and lipid- rich biomass. Fast pyrolysis of microalgae remnants after lipid extraction produced bio-oil with around 13% nitrogen content. This large amount of nitrogen can have deleterious effects on catalysts during bio-oil upgrading. Catalytic pyrolysis of protein-rich algal biomass with HZSM-5 catalyst yielded aromatic and olefinic hydrocarbons. Most of nitrogen in biomass was released as ammonia, which suggests feasibility for recycling nitrogen as a nutrient for microalgae cultivation. Catalytic pyrolysis of protein-rich biomass produced significantly higher yields of hydrocarbons compared with lignocellulosic biomass. Protein and lipid produced higher yield of hydrocarbons compared with carbohydrates and lignin in biomass. The lipid components in biomass have positive synergistic effect to enhance yields of aromatics. The effect of reactor configuration on the products of catalytic pyrolysis was also investigated. In-situ catalytic pyrolysis produced significantly more aromatics and less olefins compared with ex-situ catalytic pyrolysis. Selectivity of monocyclic aromatics such as benzene and toluene for ex-situ catalytic pyrolysis was higher than for the in-situ method. Variance of hydrocarbon yields for in-situ and ex-situ catalytic pyrolysis were explained by differences in gas flow and heat transfer for the two kinds of catalytic pyrolysis. The remarkably high olefin yield from ex-situ catalytic pyrolysis suggests the possibility of exploiting the process to preferentially obtain olefins from biomass. Techno-economic analysis was performed on an integrated biorefinery combining corn ethanol production and catalytic pyrolysis of dried distillers grains with solubles (DDGS) for hydrocarbon production. In addition to ethanol, a wide range of hydrocarbons including aromatics, olefins, and synthetic gasoline and diesel are produced from the integrated facility. The hydrocarbon products command a substantially higher market value than could be realized by selling the unprocessed DDGS. However, the capital costs and operating costs for the integrated biorefinery are higher than the conventional stand-alone corn ethanol biorefinery. The minimum fuel selling price (MFSP) for the integrated scenario is comparable to the MFSP for the stand-alone scenario. Combined with the benefit of product diversity, the proposed integrated corn biorefinery may be competitive with conventional stand-alone ethanol production